I built a small open-source CLI because AI kept struggling with SQL against a large Oracle schema. It makes AI schema-aware before it writes SQL.

The workflow is simple:

1. Search database metadata
2. Pull table context
3. Run small read-only checks
4. Save useful SQL
5. Store domain notes in Markdown

It is Oracle-only right now and still early, but it has already helped in my own AI coding workflow. Used it mainly with github copilot

Repo: oracledb-navigator

Curious if others are solving AI + database context in a similar way.

I wrote a longer post about a small internal tool I built after running into the same AI failure mode again and again:

AI could write decent SQL syntax, but it kept guessing the wrong tables, joins, and business meaning in a large Oracle database.

The real problem was not prompting.

It was context.

So I built `db-cli`: a simple read-only CLI that lets an AI assistant explore schema metadata before writing SQL.

The workflow is basically:

```text
search -> inspect table context -> run small read-only queries -> validate -> then write final SQL
```
The most useful command ended up being table context, not query execution. It returns columns, data types, constraints, indexes, and relationships in structured output, so the model can reason from actual schema facts instead of inventing joins from table names.

The bigger lesson was that AI did not need a magic database agent. It needed the same investigation loop a new engineer would use.

I wrote the full post here:

https://sahiljain801543.substack.com/p/teaching-ai-to-navigate-a-giant-database

Curious if others are building similar internal context tools for AI coding workflows.
Suggestions and Questions are welcome
Open to share code as well